Model of mobility demands for future short distance public transport systems

Short distance public transport faces huge challenges, although it is very important within a sustainable transport system to reduce traffic emissions. Revenues and subsidization are decreasing and especially in rural regions the offer is constantly diminishing. New approaches for public transport systems are strongly needed to avoid traffic infarcts in urban and rural areas to grant a basic offer of mobility services for everyone. In the proposed work a demand centered approach of dynamic public transport planning is introduced which relies on regional traffic data. The approach is based on a demand model which is represented as a dynamic undirected attributed graph. The demands are logged through traffic sensors and sustainability focused traveler information systems

Testing Autonomous Robot Control Software Using Procedural Content Generation

We present a novel approach for reducing manual effort when testing autonomous robot control algorithms. We use procedural content generation, as developed for the film and video game industries, to create a diverse range of test situations. We execute these in the Player/Stage robot simulator and automatically rate them for their safety significance using an event-based scoring system. Situations exhibiting dangerous behaviour will score highly, and are thus flagged for the attention of a safety engineer. This process removes the time-consuming tasks of hand-crafting and monitoring situations while testing an autonomous robot control algorithm. We present a case study of the proposed approach – we generated 500 randomised situations, and our prototype tool simulated and rated them. We have analysed the three highest rated situations in depth, and this analysis revealed weaknesses in the smoothed nearness-diagram control algorithm

Effects of Roads on Black Bear Distribution in Southern Vermont

The American black bear (Ursus americanus) is a wide-ranging, large carnivore species that makes use of multiple habitat types throughout the year. In the northeastern US, black bears require large areas of relatively undisturbed forest and avoid development, such as urban and suburban areas. Roads represent another form of development that may affect the distribution of bears. However, the effects of roads remain largely unknown and represent a potential conservation concern. We sought to determine the relationship between roads and distribution of black bears in a forested region of southern Vermont. We examined the probability of occurrence of black bears using GPS-collar data (n = 30,179 locations) collected from a marked population of bears (n = 8 females, 15 males) from 2011 to 2014. We then constructed a set of 7 candidate models to explain occupancy that included combinations of three road types: secondary, vehicular, and local. Model selection techniques were used to determine the best model in the set. Models were performed separately for male and female bears, which have been shown to exhibit different distribution patterns elsewhere. The top model for each sex was the most complex in the set, and included the additive combination of all three road types. For males, vehicular and local roads positively affected occupancy, whereas secondary roads had a negative influence on occupancy. For females, vehicular and secondary roads positively affected occupancy, whereas local roads negatively affected occupancy. Our results indicate that small, low traffic, residential and ATV roads influence bear distribution; most likely by providing easy pathways to travel through the forested landscape and food resources not found elsewhere. Secondary and local roads also affect sexes differently, which could result in demographic and genetic consequences. Models provide a measure of the effect of different roads on bear distribution that can help inform decision-making about development in the forested landscapes of Vermont

SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment

The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's features while taking specific requirements and constraints into account still poses a significant challenge. Existing methods have focused on worker-task assignments based on crowdsourcing, they neglect the scenario-dataset-model assignment problem. To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a heterogeneous information framework that can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. To comprehensively evaluate models, a new score function that utilizes multi-head attention mechanisms is proposed. Moreover, a novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching. Six popular traffic scenarios are selected as study cases and extensive experiments are conducted on a dataset to verify the effectiveness and efficiency of SMAP and the score function

How simple rules determine pedestrian behavior and crowd disasters

With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. Yet, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a novel cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. While simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This includes the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities-a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA